A spectral clustering approach for the evolution of the COVID-19 pandemic in the state of Rio Grande do Sul, Brazil

TL;DR

This paper uses spectral clustering on graph models of municipalities in Rio Grande do Sul to analyze COVID-19 spread and evaluate regional policies, suggesting flexible regional approaches and the impact of isolation.

Contribution

It introduces a spectral clustering method to analyze pandemic evolution at municipal level and assesses regional policy flexibility and isolation effects.

Findings

Flexible regional clustering can improve pandemic management.

Isolation measures have a dampening effect on disease spread.

Spectral clustering reveals meaningful regional groupings.

Abstract

The aim of this paper is to analyse the evolution of the COVID-19 pandemic in Rio Grande do Sul by applying graph-theoretical tools, particularly spectral clustering techniques, on weighted graphs defined on the set of 167 municipalities in the state with population 10,000 or more, which are based on data provided by government agencies and other sources. To respond to this outbreak, the state has adopted a system by which pre-determined regions are assigned flags on a weekly basis, and different measures go into effect according to the flag assigned. Our results suggest that considering a flexible approach to the regions themselves might be a useful additional tool to give more leeway to cities with lower incidence rates, while keeping the focus on public safety. Moreover, simulations show the dampening effect of isolation on the dissemination of the disease.